AdaOPC: A self-adaptive mask optimization framework for real design patterns

Wenqian Zhao, Xufeng Yao, Ziyang Yu, Guojin Chen, Yuzhe Ma, Bei Yu, Martin D. F. Wong

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

7 Citations (Scopus)

Abstract

Optical proximity correction (OPC) is a widely-used resolution enhancement technique (RET) for printability optimization. Recently, rigorous numerical optimization and fast machine learning are the research focus of OPC in both academia and industry, each of which complements the other in terms of robustness or efficiency. We inspect the pattern distribution on a design layer and find that different sub-regions have different pattern complexity. Besides, we also find that many patterns repetitively appear in the design layout, and these patterns may possibly share optimized masks.We exploit these properties and propose a self-adaptive OPC framework to improve efficiency. Firstly we choose different OPC solvers adaptively for patterns of different complexity from an extensible solver pool to reach a speed/accuracy co-optimization. Apart from that, we prove the feasibility of reusing optimized masks for repeated patterns and hence, build a graph-based dynamic pattern library reusing stored masks to further speed up the OPC flow. Experimental results show that our framework achieves substantial improvement in both performance and efficiency.

Original languageEnglish
Title of host publicationProceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2022
PublisherAssociation for Computing Machinery (ACM)
Number of pages9
ISBN (Electronic)9781450392174
DOIs
Publication statusPublished - 30 Oct 2022
Event41st IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2022 - Virtual and, San Diego, United States
Duration: 30 Oct 20224 Nov 2022
https://2022.iccad.com/ (Conference website)
https://dl.acm.org/doi/proceedings/10.1145/3508352 (Conference proceedings)

Publication series

NameIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
ISSN (Print)1092-3152

Conference

Conference41st IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2022
Country/TerritoryUnited States
CitySan Diego
Period30/10/224/11/22
Internet address

Scopus Subject Areas

  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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